Abstract: The present disclosure belongs to the technical field of artificial intelligence, and discloses an automatic classification method and system of teaching videos based on different presentation forms, which with three convolutional neural network models, may accurately locate the information required for teaching video classification by two self-trained YOLOV4 target detection neural network models and human body key point detection technology, solves the problem that the background and character features of teaching videos do not change significantly, and improves the accuracy of feature extraction. The structure of the self-built convolutional neural network models is suitable for classification of Interview type and Head type teaching videos. The depth of the network is just appropriate compared with several classical video classification algorithms, which reduces the energy consumption of computer hardware.

Abstract: The present disclosure belongs to the technical field of artificial intelligence, and discloses an automatic classification method and system of teaching videos based on different presentation forms, which with three convolutional neural network models, may accurately locate the information required for teaching video classification by two self-trained YOLOV4 target detection neural network models and human body key point detection technology, solves the problem that the background and character features of teaching videos do not change significantly, and improves the accuracy of feature extraction. The structure of the self-built convolutional neural network models is suitable for classification of Interview type and Head type teaching videos. The depth of the network is just appropriate compared with several classical video classification algorithms, which reduces the energy consumption of computer hardware.